Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

Abstract

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

“…Species are impacted both directly and indirectly by global change factors, which influence both individuals and populations. Habitat loss, agricultural expansion, urbanization, a rise in illness, pollution, overexploitation, and predation are some of the factors that have an impact on species [1,2]. In response to the distorted temperature and precipitation regime, some plant species and other categories of taxa are shifting their geological range.…”

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

“…Out of all these, data transmission consumes more energy as compared to other tasks. Energy conservation techniques in WSNs are broadly classified into three categories, namely, mobility based, data-driven, and duly-cycling 6 as represented in Figure 1. These data-driven energy conservation techniques have gained more popularity 7 as they aim to reduce data transmission and have a well-established method for energy-efficient data acquisition.…”

“…Data transmission reduction techniques include in-network data procession, data compression techniques, and data prediction techniques while energy-efficient data acquisition includes adaptive sampling, hierarchical sampling, and model-driven active sampling. 6,7 Data-driven models are used in WSNs for a variety of applications including environmental monitoring, public safety, industry, healthcare, agriculture, transportation, and military. They are divided into four basic models 8 : query-driven, time-driven, event-driven, and hybrid-driven as depicted in Figure 2.…”

“…When an event occurs, the SN detects and transmits data about only that event. 6,14 After that, when the event is over, SN will collect the data continuously or periodically. When the user, on the other hand, sends or requests query-driven data based on their requirement, the SN sends the data to the user via a query.…”

“…In the cluster-based architecture, data is stored in three locations: the SN, the CH, and finally the BS. 16 Preceding survey studies and literature review focused on specific topics for instance a review of data aggregation in WSNs, [17][18][19][20][21][22] data compression methods, 21,23 hierarchical routing protocols, [24][25][26] a survey of clustering in heterogeneous and homogeneous WSN, 26 energy-efficient routing protocol, [27][28][29][30] energy efficiency in WSNs, [31][32][33][34] network lifetime, 35,36 data management in WSN, 6,[37][38][39] machine learning [40][41][42][43] and deep learning [38] in WSN, computational intelligence techniques 44 and network monitoring in WSN, 45 big data challenges in WSN, 22,46,47 security issues in WSN, [48][49][50] multi-objective QoS optimization in WSN, 51 localization and coverage in WSN 52,53 (presented in detail in Section 2). This review article, on the other hand, intends to categorize the current data transmission reduction techniques...…”